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Shortly after de Broglie published his ideas that the electron in a hydrogen atom could be better thought of as being a circular standing wave instead of a particle moving in quantized circular orbits, Erwin Schrödinger extended de Broglie’s work by deriving what is now known as the Schrödinger equation. When Schrödinger applied his equation to hydrogen-like atoms, he was able to reproduce Bohr’s expression for the energy and, thus, the Rydberg formula governing hydrogen spectra.
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Design of an integrated model using deep reinforcement learning and Variational Autoencoders for enhanced quantum

Harshala Shingne1,2, Diptee Chikmurge3, Priya Parkhi4,5

  • 1Symbiosis Institute of Technology, Nagpur Campus, India.

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|July 18, 2025
PubMed
Summary

This study integrates AI and machine learning into quantum key distribution (QKD) protocols. Advanced models enhance secure key generation rates and detect eavesdropping, improving quantum communication security and efficiency.

Keywords:
Deep Reinforcement Learning for Quantum Communication Device QKD OptimizationDeep reinforcement learningMulti-agent systemsQuantum cryptographyQuantum key distributionVariational autoencoder

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Area of Science:

  • Quantum Information Science
  • Artificial Intelligence
  • Network Security

Background:

  • Traditional Quantum Key Distribution (QKD) faces limitations in dynamic environments due to static protocols.
  • Network fluctuations and potential attacks compromise the security and efficiency of existing QKD systems.
  • High key generation rates and robust security are crucial for advanced communication networks.

Purpose of the Study:

  • To enhance the security and efficiency of quantum communication protocols using AI and machine learning.
  • To address the limitations of conventional QKD systems in dynamic and adversarial environments.
  • To propose adaptive and resilient quantum security solutions.

Main Methods:

  • Deep Reinforcement Learning (DRL) for adaptive QKD protocol optimization.
  • Variational Autoencoder (VAE) for anomaly detection and eavesdropping identification in quantum networks.
  • Multi-Agent Deep Q-Networks (MADQN) for optimizing cryptographic protocols in distributed quantum networks.

Main Results:

  • DRL approach increased secure key generation rate by 15-20% and suppressed Quantum Bit Error Rate (QBER) by 30-40% under noisy conditions.
  • VAE model achieved 85-90% attack detection accuracy with a 25% reduction in false positives.
  • MADQN system reduced attack vulnerabilities by 15-18% and computational complexity by 20-25%.

Conclusions:

  • Integrating AI and machine learning significantly enhances quantum communication system security and efficiency.
  • The proposed models overcome critical limitations of conventional QKD systems.
  • This research paves the way for more resilient and adaptive quantum security solutions.